We study a two-stage scenario for learning to defer with multiple experts, which is crucial in
practice for many applications. In this scenario, a predictor is derived in a first stage by …

We present an extensive study of surrogate losses for structured prediction supported by* $
H $-consistency bounds*. These are recently introduced guarantees that are more relevant …

Learning with abstention is a key scenario where the learner can abstain from making a
prediction at some cost. In this paper, we analyze the score-based formulation of learning …

A series of recent publications by Awasthi et al. have introduced the key notion of* $ H $-
consistency bounds* for surrogate loss functions. These are upper bounds on the zero-one …

We study the key framework of learning with abstention in the multi-class classification
setting. In this setting, the learner can choose to abstain from making a prediction with some …

We study the problem of classification with a reject option for a fixed predictor, applicable in
natural language processing. We introduce a new problem formulation for this scenario, and …

The learning to defer (L2D) framework allows autonomous systems to be safe and robust by
allocating difficult decisions to a human expert. All existing work on L2D assumes that each …

We present a detailed study of surrogate losses and algorithms for multi-label learning,
supported by $ H $-consistency bounds. We first show that, for the simplest form of multi …

This paper presents a comprehensive analysis of the growth rate of $ H $-consistency
bounds (and excess error bounds) for various surrogate losses used in classification. We …

We present a detailed study of top-$ k $ classification, the task of predicting the $ k $ most
probable classes for an input, extending beyond single-class prediction. We demonstrate …